EPMC_plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mess of plots of SRM data coverage across REF2014 and EPMC. \n",
    "I think producing some insight would take a more thoughtful ontology, maybe query EPMC as clusters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "from matplotlib import pyplot as plt\n",
    "import pylab as pyl\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('/path/to/SRM_master.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>term</th>\n",
       "      <th>term_score</th>\n",
       "      <th>idf_score</th>\n",
       "      <th>REF_score</th>\n",
       "      <th>EPMC%coverage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>658</td>\n",
       "      <td>observation schedules</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>1</td>\n",
       "      <td>0.36506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>578</th>\n",
       "      <td>578</td>\n",
       "      <td>insider action research</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>522</td>\n",
       "      <td>small area estimation</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>3</td>\n",
       "      <td>2.63980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>136</td>\n",
       "      <td>netnography</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>106</td>\n",
       "      <td>pareto chart</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>343</td>\n",
       "      <td>chebyshev's theorem</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>86</td>\n",
       "      <td>safe fieldwork</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.04650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>388</td>\n",
       "      <td>realist synthesis</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.01602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>394</td>\n",
       "      <td>total survey error</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>1.83832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>collage inquiry</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>372</td>\n",
       "      <td>audience ethnography</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.727335</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>500</td>\n",
       "      <td>lorenz curve</td>\n",
       "      <td>1.203179</td>\n",
       "      <td>11.034180</td>\n",
       "      <td>0</td>\n",
       "      <td>0.06608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>583</td>\n",
       "      <td>maxqda</td>\n",
       "      <td>1.903305</td>\n",
       "      <td>11.034180</td>\n",
       "      <td>0</td>\n",
       "      <td>0.02658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>340</td>\n",
       "      <td>robust regression</td>\n",
       "      <td>2.191213</td>\n",
       "      <td>10.628715</td>\n",
       "      <td>1</td>\n",
       "      <td>2.92366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>570</td>\n",
       "      <td>unit non-response</td>\n",
       "      <td>3.807887</td>\n",
       "      <td>10.341033</td>\n",
       "      <td>0</td>\n",
       "      <td>8.74328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>556</td>\n",
       "      <td>box-score method</td>\n",
       "      <td>4.513736</td>\n",
       "      <td>10.628715</td>\n",
       "      <td>0</td>\n",
       "      <td>1.69354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>471</th>\n",
       "      <td>471</td>\n",
       "      <td>creative interviewing</td>\n",
       "      <td>5.174003</td>\n",
       "      <td>10.341033</td>\n",
       "      <td>0</td>\n",
       "      <td>0.32284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>data collection skills</td>\n",
       "      <td>6.634335</td>\n",
       "      <td>10.341033</td>\n",
       "      <td>0</td>\n",
       "      <td>1.66432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>344</td>\n",
       "      <td>transformative paradigm</td>\n",
       "      <td>7.282255</td>\n",
       "      <td>9.935567</td>\n",
       "      <td>0</td>\n",
       "      <td>0.03182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>555</td>\n",
       "      <td>best linear unbiased estimator</td>\n",
       "      <td>9.262358</td>\n",
       "      <td>9.935567</td>\n",
       "      <td>0</td>\n",
       "      <td>0.05444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>366</td>\n",
       "      <td>data analysis skills</td>\n",
       "      <td>11.370327</td>\n",
       "      <td>9.935567</td>\n",
       "      <td>0</td>\n",
       "      <td>3.31142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>482</td>\n",
       "      <td>diffuse comparisons</td>\n",
       "      <td>13.703352</td>\n",
       "      <td>9.935567</td>\n",
       "      <td>0</td>\n",
       "      <td>0.85714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>420</td>\n",
       "      <td>visual ethnography</td>\n",
       "      <td>13.743277</td>\n",
       "      <td>9.781376</td>\n",
       "      <td>3</td>\n",
       "      <td>0.01084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>627</td>\n",
       "      <td>virtual ethnography</td>\n",
       "      <td>14.813449</td>\n",
       "      <td>10.117873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>380</td>\n",
       "      <td>econometrics</td>\n",
       "      <td>15.823987</td>\n",
       "      <td>9.424726</td>\n",
       "      <td>27</td>\n",
       "      <td>0.04186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>contemplative inquiry</td>\n",
       "      <td>16.898238</td>\n",
       "      <td>10.117873</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>621</td>\n",
       "      <td>youth-led research</td>\n",
       "      <td>16.902790</td>\n",
       "      <td>9.530102</td>\n",
       "      <td>0</td>\n",
       "      <td>0.56990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>121</td>\n",
       "      <td>covert research</td>\n",
       "      <td>18.511734</td>\n",
       "      <td>9.424726</td>\n",
       "      <td>0</td>\n",
       "      <td>0.27914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>emergent design</td>\n",
       "      <td>20.506810</td>\n",
       "      <td>8.954674</td>\n",
       "      <td>1</td>\n",
       "      <td>0.64096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>206</td>\n",
       "      <td>nested variables</td>\n",
       "      <td>22.861783</td>\n",
       "      <td>9.088173</td>\n",
       "      <td>0</td>\n",
       "      <td>0.82562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>398</td>\n",
       "      <td>independent variables</td>\n",
       "      <td>21478.804292</td>\n",
       "      <td>2.944439</td>\n",
       "      <td>3</td>\n",
       "      <td>10.40882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>classification</td>\n",
       "      <td>22136.705147</td>\n",
       "      <td>3.401197</td>\n",
       "      <td>250</td>\n",
       "      <td>16.05242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>438</td>\n",
       "      <td>multiple regression</td>\n",
       "      <td>23020.667917</td>\n",
       "      <td>3.295837</td>\n",
       "      <td>3</td>\n",
       "      <td>11.08262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>280</td>\n",
       "      <td>life history research</td>\n",
       "      <td>23446.244569</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>0</td>\n",
       "      <td>10.52066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>315</td>\n",
       "      <td>mean scores</td>\n",
       "      <td>24559.811453</td>\n",
       "      <td>2.944439</td>\n",
       "      <td>1</td>\n",
       "      <td>10.86814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>330</td>\n",
       "      <td>sample size</td>\n",
       "      <td>24758.263666</td>\n",
       "      <td>2.890372</td>\n",
       "      <td>33</td>\n",
       "      <td>24.22886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>224</td>\n",
       "      <td>longitudinal research</td>\n",
       "      <td>26148.663259</td>\n",
       "      <td>2.772589</td>\n",
       "      <td>36</td>\n",
       "      <td>9.33398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>531</td>\n",
       "      <td>individual differences</td>\n",
       "      <td>26713.514425</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>31</td>\n",
       "      <td>24.88268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>216</td>\n",
       "      <td>gender difference</td>\n",
       "      <td>27652.911190</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>2</td>\n",
       "      <td>6.27426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>516</td>\n",
       "      <td>dependent variables</td>\n",
       "      <td>29882.185498</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>2</td>\n",
       "      <td>7.41548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>220</td>\n",
       "      <td>standard deviations</td>\n",
       "      <td>31011.855205</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>7</td>\n",
       "      <td>6.16594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>251</td>\n",
       "      <td>analysis of variance</td>\n",
       "      <td>32880.757454</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>4</td>\n",
       "      <td>17.53506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>54</td>\n",
       "      <td>survey research</td>\n",
       "      <td>32987.583745</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>25</td>\n",
       "      <td>14.04520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>412</td>\n",
       "      <td>evaluation</td>\n",
       "      <td>35866.330808</td>\n",
       "      <td>2.639057</td>\n",
       "      <td>2204</td>\n",
       "      <td>50.06850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>56</td>\n",
       "      <td>validity</td>\n",
       "      <td>36122.779612</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>234</td>\n",
       "      <td>9.01422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>323</td>\n",
       "      <td>social interaction</td>\n",
       "      <td>36702.160941</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>52</td>\n",
       "      <td>4.46812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>career development</td>\n",
       "      <td>37674.346522</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>50</td>\n",
       "      <td>2.01122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>128</td>\n",
       "      <td>population variance</td>\n",
       "      <td>40059.735321</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>1</td>\n",
       "      <td>7.02794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>control groups</td>\n",
       "      <td>40243.899047</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>11</td>\n",
       "      <td>39.14192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>75</td>\n",
       "      <td>cronbach's alpha</td>\n",
       "      <td>44738.521933</td>\n",
       "      <td>2.833213</td>\n",
       "      <td>1</td>\n",
       "      <td>0.58450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>358</td>\n",
       "      <td>reliability</td>\n",
       "      <td>45152.473784</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>344</td>\n",
       "      <td>8.51436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>361</td>\n",
       "      <td>regression analysis</td>\n",
       "      <td>48502.606721</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>15</td>\n",
       "      <td>20.61374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>effect size</td>\n",
       "      <td>48644.864639</td>\n",
       "      <td>3.044522</td>\n",
       "      <td>13</td>\n",
       "      <td>30.31592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>hypothesis</td>\n",
       "      <td>56056.287880</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>187</td>\n",
       "      <td>28.27316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>532</th>\n",
       "      <td>532</td>\n",
       "      <td>intervention studies</td>\n",
       "      <td>58176.712710</td>\n",
       "      <td>2.564949</td>\n",
       "      <td>21</td>\n",
       "      <td>14.00622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>143</td>\n",
       "      <td>factor analysis</td>\n",
       "      <td>59826.471747</td>\n",
       "      <td>2.639057</td>\n",
       "      <td>10</td>\n",
       "      <td>41.61258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>518</td>\n",
       "      <td>theory</td>\n",
       "      <td>61386.595833</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>1195</td>\n",
       "      <td>15.98070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>612</td>\n",
       "      <td>correlation</td>\n",
       "      <td>73806.944919</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>175</td>\n",
       "      <td>35.37630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>429</td>\n",
       "      <td>sampling</td>\n",
       "      <td>93380.945847</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>198</td>\n",
       "      <td>14.79144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>363</td>\n",
       "      <td>measurement</td>\n",
       "      <td>129426.007019</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>742</td>\n",
       "      <td>30.34380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>659 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                            term     term_score  idf_score  \\\n",
       "658         658           observation schedules       0.000000  11.727335   \n",
       "578         578         insider action research       0.000000  11.727335   \n",
       "522         522           small area estimation       0.000000  11.727335   \n",
       "136         136                     netnography       0.000000  11.727335   \n",
       "106         106                    pareto chart       0.000000  11.727335   \n",
       "343         343             chebyshev's theorem       0.000000  11.727335   \n",
       "86           86                  safe fieldwork       0.000000  11.727335   \n",
       "388         388               realist synthesis       0.000000  11.727335   \n",
       "394         394              total survey error       0.000000  11.727335   \n",
       "62           62                 collage inquiry       0.000000  11.727335   \n",
       "372         372            audience ethnography       0.000000  11.727335   \n",
       "500         500                    lorenz curve       1.203179  11.034180   \n",
       "583         583                          maxqda       1.903305  11.034180   \n",
       "340         340               robust regression       2.191213  10.628715   \n",
       "570         570               unit non-response       3.807887  10.341033   \n",
       "556         556                box-score method       4.513736  10.628715   \n",
       "471         471           creative interviewing       5.174003  10.341033   \n",
       "39           39          data collection skills       6.634335  10.341033   \n",
       "344         344         transformative paradigm       7.282255   9.935567   \n",
       "555         555  best linear unbiased estimator       9.262358   9.935567   \n",
       "366         366            data analysis skills      11.370327   9.935567   \n",
       "482         482             diffuse comparisons      13.703352   9.935567   \n",
       "420         420              visual ethnography      13.743277   9.781376   \n",
       "627         627             virtual ethnography      14.813449  10.117873   \n",
       "380         380                    econometrics      15.823987   9.424726   \n",
       "64           64           contemplative inquiry      16.898238  10.117873   \n",
       "621         621              youth-led research      16.902790   9.530102   \n",
       "121         121                 covert research      18.511734   9.424726   \n",
       "17           17                 emergent design      20.506810   8.954674   \n",
       "206         206                nested variables      22.861783   9.088173   \n",
       "..          ...                             ...            ...        ...   \n",
       "398         398           independent variables   21478.804292   2.944439   \n",
       "79           79                  classification   22136.705147   3.401197   \n",
       "438         438             multiple regression   23020.667917   3.295837   \n",
       "280         280           life history research   23446.244569   3.218876   \n",
       "315         315                     mean scores   24559.811453   2.944439   \n",
       "330         330                     sample size   24758.263666   2.890372   \n",
       "224         224           longitudinal research   26148.663259   2.772589   \n",
       "531         531          individual differences   26713.514425   2.833213   \n",
       "216         216               gender difference   27652.911190   3.218876   \n",
       "516         516             dependent variables   29882.185498   2.564949   \n",
       "220         220             standard deviations   31011.855205   2.484907   \n",
       "251         251            analysis of variance   32880.757454   2.397895   \n",
       "54           54                 survey research   32987.583745   2.564949   \n",
       "412         412                      evaluation   35866.330808   2.639057   \n",
       "56           56                        validity   36122.779612   2.564949   \n",
       "323         323              social interaction   36702.160941   2.833213   \n",
       "35           35              career development   37674.346522   3.688879   \n",
       "128         128             population variance   40059.735321   2.397895   \n",
       "29           29                  control groups   40243.899047   2.833213   \n",
       "75           75                cronbach's alpha   44738.521933   2.833213   \n",
       "358         358                     reliability   45152.473784   2.397895   \n",
       "361         361             regression analysis   48502.606721   2.564949   \n",
       "9             9                     effect size   48644.864639   3.044522   \n",
       "3             3                      hypothesis   56056.287880   2.079442   \n",
       "532         532            intervention studies   58176.712710   2.564949   \n",
       "143         143                 factor analysis   59826.471747   2.639057   \n",
       "518         518                          theory   61386.595833   1.945910   \n",
       "612         612                     correlation   73806.944919   1.945910   \n",
       "429         429                        sampling   93380.945847   1.609438   \n",
       "363         363                     measurement  129426.007019   1.386294   \n",
       "\n",
       "     REF_score  EPMC%coverage  \n",
       "658          1        0.36506  \n",
       "578          0        0.01728  \n",
       "522          3        2.63980  \n",
       "136          0        0.00096  \n",
       "106          0        0.01196  \n",
       "343          0        0.00452  \n",
       "86           0        0.04650  \n",
       "388          0        0.01602  \n",
       "394          0        1.83832  \n",
       "62           0        0.00120  \n",
       "372          0        0.00650  \n",
       "500          0        0.06608  \n",
       "583          0        0.02658  \n",
       "340          1        2.92366  \n",
       "570          0        8.74328  \n",
       "556          0        1.69354  \n",
       "471          0        0.32284  \n",
       "39           0        1.66432  \n",
       "344          0        0.03182  \n",
       "555          0        0.05444  \n",
       "366          0        3.31142  \n",
       "482          0        0.85714  \n",
       "420          3        0.01084  \n",
       "627          0        0.00458  \n",
       "380         27        0.04186  \n",
       "64           0        0.00416  \n",
       "621          0        0.56990  \n",
       "121          0        0.27914  \n",
       "17           1        0.64096  \n",
       "206          0        0.82562  \n",
       "..         ...            ...  \n",
       "398          3       10.40882  \n",
       "79         250       16.05242  \n",
       "438          3       11.08262  \n",
       "280          0       10.52066  \n",
       "315          1       10.86814  \n",
       "330         33       24.22886  \n",
       "224         36        9.33398  \n",
       "531         31       24.88268  \n",
       "216          2        6.27426  \n",
       "516          2        7.41548  \n",
       "220          7        6.16594  \n",
       "251          4       17.53506  \n",
       "54          25       14.04520  \n",
       "412       2204       50.06850  \n",
       "56         234        9.01422  \n",
       "323         52        4.46812  \n",
       "35          50        2.01122  \n",
       "128          1        7.02794  \n",
       "29          11       39.14192  \n",
       "75           1        0.58450  \n",
       "358        344        8.51436  \n",
       "361         15       20.61374  \n",
       "9           13       30.31592  \n",
       "3          187       28.27316  \n",
       "532         21       14.00622  \n",
       "143         10       41.61258  \n",
       "518       1195       15.98070  \n",
       "612        175       35.37630  \n",
       "429        198       14.79144  \n",
       "363        742       30.34380  \n",
       "\n",
       "[659 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='term_score',ascending =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**EPMC PLOTS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1159b45f8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.sort_values(by='EPMC%coverage', ascending = False)\n",
    "data.plot.line(x ='term', y='EPMC%coverage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "score_term = data['term_score'] > 5\n",
    "\n",
    "data = data[score_term]\n",
    "data = data.sort_values(by='EPMC%coverage', ascending = False)\n",
    "X = data['EPMC%coverage']\n",
    "Y1 = data['term_score']\n",
    "Y2 = data['idf_score']*13000\n",
    "\n",
    "plt.scatter(X,Y1,marker='.')\n",
    "plt.scatter(X,Y2,marker='.')\n",
    "plt.legend()\n",
    "plt.xlabel('EPMC%coverage')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4DPzo+e0DOlYfF5PbSdKQularitpjK0QsYuzssidKCvBIIdytpC4mZTTSX6uN7wNLgVzgBWPMUuCrIjIb+E8sCyBhjDGvAa/Zj3fb5xqWRGY5OhNlmusDdS1dLJxSHBKIWAwpR0z3HRnhAhGWudTYoRaEMvroTyCasALSeUCo3bcxZgcDFIdk4QsE43bnJBKLiKyDcIjsGJtOaa/GGKsP05j4XEyOiBxp66a500dhTmYKR5k6wt1KGoNQRiP9xSCWYbl7MrBqIIYVAWOY/c3n2BvhyuhvCkskVTMw2vI7gbbuAB2+AOMLewQilj9DuBsqWqX5SMCpfcjwCE0qEMoo5KgWhDGmHrh7kMYSN4FAYpN2QgLRx8Vk/Y5qMKSBoNQ2W0VyicYgAPYebmdBZVFKxpdqnLjDlNI8GjRIrYxCht2qcMmgPy9PIi6myCym6IVy6eNjcmogysbkJBSDANhzeORmMoUEYmzesAlSt3b52T/CYzvKyCEtBWIwXUzpIwd9qWu1BaIgG08CMQgY2S6mhvZuCnIyGJufPSxabXT7g1xz/zuc/ZPXeeq9g0M9HGUUkJYC4ZDMgHEfC2Lke5D6JWRBFGSHgvGxWBDhbqi9R0auBdHU4aM4L5PivMxh4WL6wbNbeW9/I1PH5vGvf1zH//5tt67PoaSUtBaIaCTDguhp1ueuQunwta1t6SLTKxTnZoYsiJhiEGGPIxMIRhKN7d0U52ZRkpdJe3eALn9gyMby7MZqHvrHHm44fTpPf/EMLjq+gv98div/8cyWuIs4FSVWYlpyNN1ILM3VvVAurV1MLV2MG5ONxyNhMYhYXEzWPhOLcqhu7qTTFwi16hhJNNoWRFGetTRJU7uP8YWD/zn21Ldxx+MbOHFyMXdeOJesDA93X7WQ8sIcHnjjAw41d/LTK08ckX9jZXijFkSMRApEiDRWCGctaiDOILX1e9q4fIyBAw0j04poavdRlJtJSZ5Vx9E4BHGITl+AWx5eS4ZX+NU1i8jKsL6yHo/w7xfP498uOpZnN9bwmQdWaSquknRGpUAkQuSCMdFcLelUKOcUyQHxFcrZCjFtXD4Ae+pHpkA4FkRxrmVBDEV32u8+vZmt1c389MoTqCzO7fP6TR+awd2fWsj6/Y18/Df/oKqxY9DHqKQvo1IgEvHYdvkjBML+nU5prZHUtnSFVpKLp1DOYfpYSyD2jsC0zGDQhGIQxUNkQaxYe4A/rtrPrR+dyVlzJ0Td759OmMjyG5ZS09zJ5b9+ky0HE+rEryh9GJ0CkYCPqc+Sk2m+olwgaDjS1mNBxFMo51gZpflZFGRnsG8E1kK0dvsJGkJZTDC4HV13HGrhmys3sXR6KV8595h+9z915lge/9xpCMKV977FmzsHtGS8ogBpKhD9TWKJWBC+QGSQ+ujN+kZ69uHhti6ChgHFIDwemDoub0R2dXX8+cV5WZTYQerBKpZr7/Zzy8Nryc/2cvenFpLhje1rOqe8gJWfP43K4lyue3AVT64f8JIqyihnRAtEZ79ph1FSUBOYvKMtWp+uFkR4DQQQZ6Gcsd8jTC3NH5FdXZ26h+LcTPKyvGR6ZVAa9hlj+ObKTeyqa+XnVy0MLfUaKxVFuTz2uVM5aWoJtz2ynt+8vktrJZSEGdEC0X+Ofc8Xo/dEHv8XpjsyBpHm37kegbAmqPgK5Qi9Z8rYPA40tOOPIrDx4AsE2XigibYuf/87D5DGkAWRiYhQnJdF0yC0/H5k9X5WrqviS2cfw+mzxiV0jKLcTJbfsJSLj6/grue28d2nt0TPwlNGDYdbu3h09b64bhjSsg6iw2dZFi9treUrj67np588EaFHFhKZ3Lsjs5js35FB6nQxKGoj1qIGy4qIrVmfY0HAtLF5+AKG6qZOJpfmDWhMz26s5rZH1uP1CPMqClk8rYTFU0tZPK0k7jvt/nAC0k78oTg3k4a21FoQmw828e2nNvOh2eP44lmzBnSs7Awvv7hqIRVFOfzv3z+gpqmTn12ltRKjjU5fgJe31rJy3QFe216HP2iYVxF788y0FIjbHlkferxiXZUlECIhZUgsBuFuQaS7i2ncmHCBkLh6MQnClFI7k+lw+4AFotmetD9z6lS2VbfwyKr9PPjmHgAml+ayZGopJ00rYcm0UmaVjcHjSfziNNkupiI7xbU4LzOliwY1d/r4/MNrKc3L4mefPHFAY3fweIRvXjSPiqJcvveXLXz6/ne4/9rFFNsxFSU9CQYNa/Y2sGLtAf6ysZqWTj8TCrO58YzpLFtUydzywpiPlZYC4Ub41y0hC8IfXwwikWrt4URdSxcF2RnkZvXccVoC0f97g2EWxNSxlijsPdLGGSTmMnFwEgVuO3s2xXlZ+AJBthxsZvWeI7y7t4G/7ahnxTorMFuUm8lJU0tCVsbxk4riunt2XExFubYFkZeVsi6qxhjufGID+xs6eOTmUxgbJsrJ4IYzpjOhMIcvP7qeK+75B8tvWMqkkoGJtTL82F3Xysp1VaxcV8WBhg7ysrxcML+cZYsqOW3mOLwJ3HSMHoEI+9sklubqnsWUrtS1dlFW2HuiEoktSB0egygvzCErw5OUnkyOH93J6sn0ejhhcjEnTC7mpg9Z13Xv4XbW7G1gzZ4jrNnbwCvbrIUQs7weFlQWsmRaKYunlXLS1BJK86PfSTd2+MjP8oYql4tzM9mYoiD18n/s4dmNNXz9wrksmVaaknNcdHwFY8dkcfPv1nD5r//Bg9cvYf7EkblOh9LDkbZuntlwkBVrq1i/vxGPwOmzxvHV847hvHnl5GcPbIofRQLRE4VIyMUUNUidnj6muuaeGgiHMC/dUQm3IDweYUppHnuTUAvhs5d9zYhyJyQiTBuXz7Rx+Xz8pEmAVf387t4GVu89wpo9DTz45h7u/dtuAOaWF/CHfznFVSga2329XDEl+VkpcTGt29fAfz67lXOOHc+/fGhG0o8fzikzxvL4Ladx3W9X8cl73+Y3nz6JM2YPzKpTBp8uf4BXttbyxNoqXtteiz9omFtewDc+NpdLT6xMajxu9AhE2OOkBqkjFwxKE72oa+1i/sTevkqPSFwryjmZT1NL85JjQdhWXDymckl+FufMm8A586xK5E5fgI1VTby45RD3/W037+5t4Nx5fauUmzq6QwFqsFxNnb5gUhsPNrZ384U/rGNCYQ4/+URy4g79ccyEAlbcejrXPbiK6x5cxX9/4niWLZyU8vMqA8MYJ65QxV82HKS5009ZQTbXnz6NZQsnMW9i7HGFeBg9AhHuYkokzTUQxBjTp713muhBH8Ib9TnEGoNw/r7OfDd1bD5v7T7s+veLB5/jYhrARJqT6WXJtFKOrSjkvr/tZntNs6tAWBZEj0CEF8uVFw1cIIJBw1cfe4/alk4e/9xpFIWdK9WUF+Xw2OdO5bO/e5cvP/oe1U2d3PKRmQO6Nkpq2FPfxop1Vfx5XRX7jrSTm+nl/PkTWLZoEqfPHBtzEWWijB6BoJdCxI0xlg88w5tAU6IRRnu3n9Yufx+BiDUGEaqkdiyIsXm0dweoa+1ifEHi5m8gGMTrkaRMZGOyM5hcmsu2mhbX1xvau3tlezhi0dDeTXnRwE34+/6+m5e31fLdS+ZzwuTiAR8vXgpzMnnohiXc/qcN/Oj57dQ0dfLtf5qfUCBTSS6N7d08vaGalWsPsHZfIyJw+sxx3Hb2bM5fUM6YAcYV4mFUCEQgaCIsiMTwBQwZ3t7HiJrFNIL1oy5UA9F7IrRcTP2/3xER528zxc5k2ne4fUAC4Q+YAVkPkcyZUMj2KALR1OHrdVff049p4IHqVR8c4b9f2M5Fx1XwmVOnDvh4iZKd4eVnnzyRiqIc7v3bbg41d/LzqxZqrcQQ0OUP8Oq2OlasPcCr22vxBQzHTBjDnRfO5dITJ1JR1LeT72AwKgTiV6/uHHAMAiw3Uy7eXseIp5vrN1Zu5IRJRXxyyZTEBjBIRLbZcPDEnMXkCIT1t5lmd3Xdc7idxQPI0vEHkysQc8sLeHV7LV3+ANkZPZOiMcZyMeWGCUSu42IaWKC6vrWLL/5xLZNLcrnriuOG3K3j8Qhf/9ixlBfl8B/PbOGa+9/h/s8spuQoGV5KcjDGsHafFVd4ZkM1TR0+xo3J5jOnTmPZwkrmTywc8v+PUSEQu+pae/2hE01RdauFiOf6vbTlEB3dgZEjEGPcYhCxB6mdubyyOBePMOCurv5AMKk+1znlBQSChp21rb1SPtu6A/iDpncMIn/gLb8DQcOXHllPY7uPB29dSkHO4MUd+uP606dTXpjDbY+u54rf/IPl1y8dcGGj4s7ew22heoW9h9vJyfRw3rxyLl9UyRmzxqU8rhAPo0IgILIOIrFjhFdTR18wKLpidPmDUZv+JYN39zZQNiY75NJJlLpWdwtCYi6Us347MYisDA8Ti3MHvC5EKiwIgO01Lb0EojHUqK/nLjq0aNAALIi7X9nBGzvr+eEVx6Us62QgXHhcBeMKsrlp+Rouv+cfPHjdEhZUaq1EMmhq9/HMRqte4d29DYjAqTPG8oUzZ3HBgvJhdbMQzqgQCKF3tlF7d2KLz/cSiLBjx0q3P5jSpmm3PbKOU2aM5cefOGFAx6lr6cLrkT71AbH2YgrFIMK2TRubP+C23/5AWJJAEpg2Lp8sr6dPHCJURR1mQeRmecnO8CS8rOcbO+r5+cs7uHxRJVcunpz4oFPMkmmlPHHLqVz729V88t63uOfTJ/HhY8qGelgjkm5/kFe317JybRWvbKulOxBk9vgx3HHBHC47sZKJLisEDjdGhUBA7zv7z/3fuzx808lxd8sMr6aOzPWPJHIaNcbQ5Q/0qchOlKvue4tzjp3ATWHFVc0dvqQsalPb3MXY/Kw+GS0iEIzBAApGxCDAClQ/t7F6QOOyLIjkmd+ZXg8zx4/pk8nU5DTqy+19V1ecl5lQkPpQcye3PbKOWWVj+P5lC4bcr9wfs8YXsOLW07juwdXc8NBqfnjF8VxxktZKxIIxhnX7G1m5topnNhykod3HuDFZXHPKFC5fOIkFlUMfV4iHUSQQvZ9/56nNvPiVj8R1jFj879HwBw1BA/5YZtgY2FrdQnFuFjd9qGdbhy9Ac+fAW2HXtfatgQA7iymW+E1EDAKsYrmGdp+VHZSbmDntDwaTakGA5WZ6a9fhXtsawxYLCqckLytuF5M/EOSLf1hHe3eARz+7iLyskfGVm1CYw2OfPYXP/d+7fPVP71HT3MmtH9VaiWjsP9Ieiit8UN9GdoaH8+aXc/nCSs6YPY7MYRRXiIeR8d+aBCLv/HbUtrK7rpUZZWNiPka4QMQrFU6AO1kuJn8gSHVTzwL1vkAQX8CEOp4OBLciOYinWZ+9f5hCTLUzmfYdbue4SYn5tf1Bk/Q8/TnlBaxcV0VTe09aq9NSoySieK0oNzPuIPWP//o+q/Yc4WefPJFZ4wuSM+hBoiAnkwevW8odj7/Hf7+wnYONHfzHpQu0VsKmqcPHsxurWbH2AKv3NABwyoxSbvnITC44rpzCYRpXiIdRIRDR7nrO+snr7LnropiPEz65m4hc//7osgUiWUFqf9BaY8HBiau0JMOCaOni2Iq+k1nshXJ9YxDhXV0TFohAkMwkupjAEgiAbTXNnDxjLNBzM1EYYemU5GWxu7415mO/vPUQv3l9F1efPIXLFlYmacSDS1aGh59eeSIVxbnc89oualu6+MVVC3t1+R1NdPuDvP5+HSvXHeClrbV0+4PMLMvn9vPncOmJE9OuS+6oEIhk4eYdilYHETmPdtnLo/qTFIPwBw11rV34AkEyvR46QgIxMAsiGDTUH83FFFehXFgMwk6ZHEhPpkAKLIhQJtOhljCB6CY309unYCyeGMSBhna+8th7zKso5FsXz0vqmAcbj0f42gVzqSjK4dtPbebq+9/mgWuXHLUbbjphjOG9A02sXHuApzdUc6Stm9L8LK5eOoXLF1VyXGVR2rreRoVADPTSZXk9dAeCvV1McS4Y5LiYfEHD+4dauOSXb/Dilz+SUK65MSZkzRxq7mRSSR7t3Zbl0NLlJxg0CTd+a2jvxh80fWogII5CubD9HfKzMygryB5QV1dfwJCZ5BhEeWEOhTkZvQLVkX2AzjLnAAAgAElEQVSYHIpsgeivp1S3P8jn/7COYNDw62sWpU1l8mdOncb4ghxue2Sdta7E9UsHnFI9nDnQ0M6f11WxYl0Vu+vayMrwcO68CVy+sJIPH1M2YuMK8TDon1BEJovIqyKyRUQ2i8ht9vZSEXlRRHbYv0sGe2zRcCalgMvk+MAb1nKO/dEVikEE2XGolU5fkNqW/t/nhj/M1eWc23ExGQNt3Ym7mXpqIPq2xIi5WV+o3XfvSXSgXV1TYUGICHPLe7fcaIwSSC/Jy6I7EAwtaRuN/3puK+/tb+RHHz+eaePykzreoeaCBeU8fNPJHGnr5vJ73mTjgaahHlJSae708ciqfVx571uc8cNX+fFf32fcmGzuuvw4Vn/zHH519SLOPnbCqBAHGAKBAPzAV40x84BTgM+LyDzgTuBlY8xs4GX7+ZAR/o+faS8aEwyPQYSFqd/e3ZMFE+3GsstnCYQ/YGi23UCJJkWFx0KcOET4pDWQOESoD1NhXwsi5hhEsGf/cKaMHZhA+ALBpKa5OswpL+D9mpaQsDVFsSCctNeGo7iZnttYzYNv7uH606dx4XEVSR/rcGDxtFKeuOU0sjO8fPK+t3hte+1QD2lA+AJBXt56iM//YS1Lvv8Sd67YSH1LF1899xj+fseZPPbZU7lq6ZSEs+9GMoPuYjLGVAPV9uMWEdkKVAKXAh+1d1sOvAZ8bbDH5/BPv3wj9NiZlMLvnsPnSSe+cDS6A9Y+vkAwlGefeNPAnmCIk8kUXvzX3OljIokV4dQ2u7fZgNjXgwhGsSCmjc1nxdqqhNdUaO70M25M8v3ec8oLaOnyU9XYwaSSPBo7upkxrm92W3FeTz+mSpcipz31bdzx+AZOnFzM1y88NunjHE7MGj+GlXatxI3L13DX5cfxiWFcABiJMYaNVU2sWFvF0+8d5HBbNyV5mVy1ZDLLFk3ihEnpG1eIhyGNQYjINGAh8A4wwRYPgBqgb5P+hE80sLdnOS6mXhZED9HWqw7HsSACwZ5U1KRaEGFupQFZEFHabEDshXLROt06mUz7j7Qze0J8KZ9VjR1srW7mjgvmxPW+WHAytrZVt1gCEc2COEpH105fgFsfXovXK/zqmkWhpUrTmfGFOTz62VO49eG13P74BmqaOvnCWbOG9cRa1dhhxRXWHmBXXRtZXg/nzBvPsoWT+MgxZaPiusXDkAmEiIwBngC+ZIxp7tVMzxgjIq7Tp4jcDNwMkFU+azCGGvqnieZe6XIRiMiCsp401x4XU6KEV2NHxiBgYJlMdS1d5GV5Xdeyjb1Zn7sF4WQy7Tkcv0A8v6kGgAsXJN9tc8yEnkyms48db8UgXAQifNGgSL779Ba2VDfz2+sWu1oX6UpBTiYPXLuEO1ds4Ccvvs/Bpk6+d+n8YdVwrqXTx3Mba1ix7gBv7z4CwJJpJdx4xgwuOq5iUBdrGmkMiUCISCaWODxsjFlhbz4kIhXGmGoRqQBcHZvGmPuA+wCyK2YPyqoLzj977zqIntfdBCISZx9/MEhTh98+RmLDDx/HQVeBGFgMws16ACugG8uIneFF3kg6bb8TyWR6bmM1x1YUMj0FQd+CnEwqi63Fgzp9Qbr9wV6N+hyKIwrpHFauO8AfV+3jlo/O5Ky5yTN8RwpZGR5+8okTqCjK4Vev7qK2uZO7r144pFXj/kCQv++oZ8W6Kv66uYYuf5BpY/P48jnHsGxhZVpnXyWTQb+CYpkKDwBbjTE/DXvpKeBa4C7795ODPbZoOBkLvSupex539ZPVAj1xil4upgTH48QgvB6hxo5BdITHIAZQTV3X0sX4KAIRa7O+nnbfvRWiOC+TgpwM9sXZ1bWmqZM1exv46rnHxPW+eJhbXsD2muZQK43IKmogFKQMtyB2HGrhGys2sXRaaUrHN9wREW4/fy7lRbl8+8lNXP2/7/DAtYsZ6xLLShXGGDYfbGbF2iqeeu8g9a1dFOdlcuXiySxbVMnCycXD2v01HBkKiT8d+Gdgo4ist7d9A0sYHhORG4G9wJXJOmF/i/q8ubOeLn8g6t2fE4Nwq4OA3hZEtHO5uZgGGoOYWJzDgYYOfIFgRJA6cQuitqUzVF0cSeytNnqvSe0gIgl1dX1hs+1eSmFW0JzyAl5/vy6UxeUWg8jJ9JKb6Q01RGzv9nPrw2vJz/Zy99ULh5VbZaj451OmMqEgmy/+0a6VuGFpqM1KqjjY2MGf11excm0VO2pbyfJ6OGvueJYtquTMOeM1rjAAhiKL6Q2ih43PTsU5+6s3uOb+dwCitt3IDLmY3N8fl4spLIspUZyGf5NL8th/pIPali7afX7ri2ASdzG9su0Qu+raOCNKl9tYC+V69ul7maeMzWNzVXy5889urOaYCWOYNT72vlnxMqe8AH/Q8O5eq6dOkYuLCSzLosEulvu3lZvYWdfK/914MhMKB75Odbpw3vxy/vAvp3DT8tVccc8/eODaJUlfd7u1y89zG6tZua6Kt3Yfxhg4aWoJ/7lsARcdV9Gn0aKSGKOikvrvO+oH9H43F1M4rgIRsWt3KAZhaHZiEAk6mZxCucklecBhapo66OgOkJflJcMjCQXBmzp83PDQGsA9gwliXzAockW5cKaW5vHCppqYV4era+li1Z4j/OtZs/s/8QCYW24t4PPOB1ZNi5sFAVCUl0Vju49HV+9nxboqvnzOMXG3jR8NnDS1hMdvOY1rf7uKq+57m19fs4gz544f0DH9gSBv7Kxn5boqXthcQ6cvyNSxedx29myWLaxMuaUyGhkVAjFQXAvlwsQiljqIUC+mYFgWU4IuJqef06QSK1umuqmT9u4AeZlesjO9CVkQWw42hx6Pd6mihjhiELhnMYEVqPYHDQcbO2MKFL6wuQZj4MLjyvvddyDMKMsn0yus+sDKcokmECV5mWw52MTfdtTxodnj+MJZg5NJNxKZWTaGFbeexg0Pream363hv5Ydx5VL4quVMMawpbqZlWurePK9g9S1dFGUm8kViyZx+aJKFk0p0bhCClGBiIEsl1YbcWcxhdVBODGE/qba5k4fXb5gnzv6kAVhp43WNHXS0R0g105PTSTNdfPBHrdPNAsi1jRXp1bCTSCmhHV1jUUgnttUzYxx+cyJMy02XjK9HmaW9Swe5JbFBJZwHGzqZEJhNv/zyRO19XU/jC/I4ZGbT+XWh9dyxxMbONjUwW1nz+53Uq9p6gzFFbYfaiHTK5w5ZzyXL6rkzLnjyc5Ij/5Wwx0ViBjocTG5v+5M/hC91UZ3Am2+v/PkZjYfbOaFL3+413a/fazS/CzysrwcbOykvdtPXlYGhTmZCWUxbaqKUSDiWlGu72uhtt+H2/lQP16jI23dvL37CJ/7yIxBuUucU17AtpoWsjI85GS6u7/KxmTj9Qi/vHoR4wYxQ2ckMyY7gweuXcydT2zkZy/toKapk+9ftqCPi7Gty8/zm2pYua6KN3fVYwwsnFLM9y5bwMXHVVAySrrHDidUIFz4/dt7ez0PCUSUSuqYXEw+l2K6fm7Gt9a0sP1QC43t3b2Cbo4FkeEVyotyqGnuoN22IApyMjjUHH8TwE1hLqZoAkGs3VyP0ul2QkEO2RmemGohXtxSQyBoUlIc54aTvVWcmxlVkD5/5iwuXWi5NpTYyfR6+PEnjmdicQ53v7KT2pYufnn1QrIzvLxpxxWe31RDhy/A5NJcvniWFVdIRd2LEjsqEC788Z19vZ5nxlEo90aUgLibiBwtSG2MCU2iGw409Vo43olBZHg8VBTlUN3USSBoKM3PoiAnI+4YRHu3n111PQvhjI1yp2ZlMfV/vKPFIDweYUqMXV2f3VjDlNI85k8s7P+kScBZGyJa/AGs9hLjNWMpIUSEr543h/KiHP79z5u45Jdv0tzho7ali8KcDC5bWMnliypZPFXjCsMFFQgXIv83M93qIMIL5cIFYme9/Xpv3Po1He1mvL61O1Tb8N7+xt4CYft5MrxCRVEub+yoZ0xOBpNKvBTkZMadxbS1urnXWKJlF3lECMTgYwotORrlSz51bF6/xXJN7T7e3FnPjR+aPmiTxRw7kyla/EFJDtecPJXxBTn8xzObOX5SMVfYcYV0WTcjnVCBcKGvQBw9zTWmZn1R9qlv7SLT4+nTD2bfkR4XzHsHGnu91mNBCBVFOXadRw65mVYMor07EHMaKcDmMPfS0Yi3UC7avD51bD5v7jx81IV3Xtx6CH/Q8LFBci8BTCzKoSAnQ3vzDALnzpvAufNGX1uSkYYKhAuRd75uhXLxtvt2dzHB4u+/hAh88F+9i/T21Ft32AunFLN+f1OvyTQUg/B4KC/KIWigprmTPDsGAVYhUazFQpuqmhibn8XdVy88akZW7GtS9+zvxtSxeXT4AlZbjyjumuc2VlNZnMvxCa5fnQgiwtcvPJbKktHTbE9RjobWoIfh5PhHzmuZGW4uph7cAtD3/W13r5Xm3F1Mdrqry5y790g7HoGLjqugvrUr1JQPeruYJhb1TGbhAhFPHGJTVTPzK4s4beY4zpwTvZgpVgvC+UDRXEyh9amjuJmaO338fUc9Fy4oH3Rf9NUnT+EjYe48RRnNjHqB2BDmvgm10Y6YlLLcXEzGPQYRzpq9R466z9Hm2n2H26goymXJtFLAikM4BII9Lqbyop478NwsL4V2Q7lY23l0+QO8f6iFBTEEgmMtlAtZEFFed7q67ql3z2R6ZWst3YFg2q7IpigjhVEvEJf88s3QY+fOvI8F4ZbFFPZ6NBdTeOO+/orpOiM6wu453M60cXnMrSggy+vpJRCOkGV6rSwmh0QsiPdrWvEHDfMn9u/KiblQrh8LorIkF69Hogaqn91YTXlhDguT3L9HUZT4GPUCEY7P7x5c7bdQLsrkH34c10B22PGcpT4d9h1pZ0ppPtkZXo6dWMj6XhZET7vvotzMUFFXrl0oB7EvGrTJrqBeUNm/BSExF8pZv6MJRKbXw8TiHNdU19YuP6+9X8cFC8rxaJWyogwpKhBh+KJaEHYMIkodRLc/iDGG+d96Puqxu/yBUNvhDHviC0+VddaWBssHf6StO1R1fOKkIjZWNYUsGMeCyPAKIj1xiLzMHgsi1pbfmw82UZCTEYoLHI1Yg9ShWM5R/rumjc13LZZ7dVst3f4gH1P3kqIMOSoQYTgL8UQGRp2JPRClDgKsVhpt3dGzmbr8QfKzrDxvZ+GZ8Lm2Jqz6eZ99Zz3VnrRPnzWO9u4AX1+xgf96bisPvPGBNS7bsnHiEHlZ3vgtiKpm5k8sjCkYbMUg+j+m6ScGAVag2i1I/dymasoKsjlpqlYqK8pQowIRxree3Az0ndgyPC4rykVMlK5B6Agrw1mC0RGIcMIznhzfvNPM7tx5E7jutGk8tuYAv33jA8oKsvn3i+eF0lgdgcjN8jImjhiEPxBka3UzC2KIP4DlMoqlRXl/MQiwUl0b2300ha3O1t7t59VtdVwwv1yb4CnKMEDrIMJ4ccsh4CiV1FGC1BCl11JEtXWZ3dytwMWCqA4TiIONlrtpUkmePR7hWxfPY9nCSmZPGNNnrd+Qiykrg0yvh9xMb0wN+3bVtdHlDzI/hvgDxJ7m6uxydIGw16c+0sbxeVYw+vXtdXT4Ailv7a0oSmyoBRHBoeZOVu9p6LXNcTF9UN/jEumIcCe5FsKFF9P5AuRlWy6mYlsgwl1WNb0EotN2F/UIgccjnDC52HUh+HAXE0Bhbmz9mJwOrrFaELEXyh29khp6d3V1eHZTDWPzs1hqp/YqijK0qEBE8Fvbvx+Ok8X0xNoDoW2tXb0nYDcXUzCiViI/wsUUnjYbHoOoae6goign5iKxM2aN48PHlIU6XxbkZNLS1b8FselgEzmZHmaUxbaUp9WLaWDdXB2coLjjTuv0BXhl6yHOm1+uazsryjBBXUwRuLVZcHOVOG4gh8g6hnACQYM/aMjP7h2k9gejWxAVRbG3e5g2Lp/f3bA09LwgJyO0rOnR2HywmXkVhTH7+8cXZFPT1EkwaI6aguq44o7mYsrLymB8QXaoWO7NnfW0dQf4mLqXFGXYoLdqEbhNam4TaFuEBdHuksHkWBBODURlcR5ZXg8zy6w7faeeITvDQ21LZ2ghoJqmzl4V0vFSkJPZbxZTMGjYcrCZBZWx9zqaUTaGLn+Qg00dR90vlhgEWG4mJ5PJsSRidXcpipJ6VCAicHMVZbgIROSiOpGCAT2uFic+Makklw3fOY8l0y0fuy9sbemgsVp8+wNBals6mTgAgSh0WRPCHwjy1801vGm3I997pJ3WLn9cE7Ljwtpdd/TFfnqymI5+vCml+aGUXsfiKchRo1ZRhgv6bYxgn0vxVrg7JRA0eD3SJwbR1hU9SO1YENZSlt5QCw7Hn19ZkseuujZ21rYSNPkEDZTH4WKKxFoTwhpfU7uPR1bv43dv7aWqsQOPwA+WHUd+tnXp58WxGI9j+XxQ39ZrfYpIHBdTfzGUaWPzeGJtJ52+AM2dPsZkZ2j8QVGGESoQESx/a2+fbd6wia6pw0dpfpaLQPS1IJw7aSdbybFEnMOFBKLYshY+/cA73PzhGQBUFA/Mgmjq6OYbKzeycm0VHb4Ap8wo5ZsXHctja/Zz54qNzBo/hkyvcMyEgpiPW1aQzZjsDHaHrT4XiT8Q5PnNNTEtFenUeew70k5zh0+tB0UZZug3MgY8YTe1R9q6LYGIcOFECgYcvVsrhAtEj7XwpzX7AXo14YuXorxMfAHD4+8eYNmJlVx72rSQpXDOsRP48mPr+cuGahZUFoZSeGNBRJg+Lp/dUbqwAjy25gDvH2rlnmsW9Xu8UC3E4XaaO32hKnBFUYYHKhAxkBGmEI3t3UBfQXCzIAJBw13PbePZjdW9tjsWhNPaY9yYnnhGg11ZHE8WUyRXLp5MaV4W580vpzRifemsDA+/uGohcycUxJzeGs6MsnzWRNSJOLR0+vjpi9tZOq2UCxb0n400LVQL0UZzh5/CXP13VJThhH4jYyDcLe5M4JFBYLc+TF9fsdH1eJExiMgsqcgiuXgZNyabq5ZOifq61yN88ezZCR17xrgxPPXeQTp9gT5rCP/6tV3Ut3bz2+uOjamGozgvi8KcjJAFUR5ldTlFUYYGjQjGQHi6ZkNbN93+YJ9sJzcLAnoqhsNx3DqOqEQKRDxFcoPN9LJ8jLEC1eHsP9LOA298wOULKzl+UuzrOEwdm8+ew22Wi8mlR5WiKEOHCkQMhE/gDe3drmIQTSDclvB0UmQP2cVxkQIxMwHXz2DhZDJttNt0OPzohe14BG6/YE5cx5s6No99R9pp6fRrkFpRhhkqEDEQbkEcae8OxR++dsFcXr/9o4B7kBrgtJlj+2zLz/KSm+mlurlHIMI7vM4aP3wF4tjyQmaU5fPgm3tC6z68u7eBp987yM0fnhl37GTq2DwONHTQ3KFBakUZbqhAxECGt0cgGtt8NNtVytPH5TN1bD5ej9DW7S4Qs8aPCQWle34LZQXZPRaECBPDMpmGs0B4PMLnPjKTrdXN/G1HPcYYvvfMFsYXZPNZO0U3HqaW5hMIGoIGDVIryjBDBSIGwusgGtq7QymujkskO8NDq0uhHFgtu91WaxtfkB1aRc7rkV6V07PHx16bMBRcdmIl5YU53PPaTp7eUM36/Y38v/PnhIrv4iE8RqMWhKIML1QgYsATEYNw3EljsnsEoj2Kiykrw8Pkkr4CUVaQHap29nqEM+f2xCpmju+/yGwoycrwcNOHpvP27iN868lNzKso5IpFkxI6llMLAWiQWlGGGSoQMdDbgvDx+vt1AKHV27IzvFGD1ACTbQuiM2xRofBeTh6PhGIVlcW5rms+DDc+tXQKRbmZNLb7+LeLj014BbjxBdlk21ldGqRWlOGFfiNjoFcWU1s3v7PbcZTaS35mZ3pobO/bPfXRm08BYHKpFV840NCzOE5ZWHFchkdCaa3DOf4QTn52Bt+6eB6761s5bea4hI/j8QhTx+bx/qFWdTEpyjBjWFkQInKBiGwXkZ0icudQj8fB6xF+dfUiPnZcOY0dPs6fP4GSvExK7Crl7AyPa3vtk2dYVkHk4jjQ24LwioQ6n44UgQC44qRJ3H7+3AEfZ0qp5WZSF5OiDC+GjUCIiBf4FXAhMA/4lIjMS/Z54uk95JCflcFFx1ewaEoJgaBhV10bx1b0dEHNzvASNHDRcRUcP6lv++yTp1tCcXmYnz7SxZRrVyWHH3e04ASqB1I9rihK8hlO38ilwE5jzG4AEXkEuBTYksyTLJlWws+vWsji778U83ucleBKbJfSztpWjg9baOeWj86k0xdg2cJKOn1Bjv3W873eX1aQzZ67LuqzzcHrEcYX5rDy1tM4Lo4FfNKFc+dNYO/hdorzsvrfWVGUQUNMDIvQDwYi8nHgAmPMTfbzfwZONsZ8IWK/m4Gb7acLgE2DOtDUMg6oH+pBJIl0+iyQXp8nnT4LpNfnGazPMtUYE31RF5vhZEHEhDHmPuA+ABFZY4xZPMRDShrp9HnS6bNAen2edPoskF6fZ7h9lmETgwCqgMlhzyfZ2xRFUZQhYDgJxGpgtohMF5Es4CrgqSEek6Ioyqhl2LiYjDF+EfkC8ALgBX5rjNncz9vuS/3IBpV0+jzp9FkgvT5POn0WSK/PM6w+y7AJUiuKoijDi+HkYlIURVGGESoQiqIoiisjViCGa1sOEZksIq+KyBYR2Swit9nbS0XkRRHZYf8usbeLiPzC/hwbRGRR2LGutfffISLXhm0/SUQ22u/5haR4fVIR8YrIOhF5xn4+XUTesc//qJ1UgIhk28932q9PCzvG1+3t20Xk/LDtg3odRaRYRB4XkW0islVETh2p10ZEvmz/j20SkT+KSM5IujYi8lsRqRWRTWHbUn4top0jBZ/lv+3/sw0islJEisNei+tvnsh1TQrGmBH3gxXE3gXMALKA94B5Qz0ue2wVwCL7cQHwPlbrkB8Bd9rb7wR+aD/+GPAcIMApwDv29lJgt/27xH5cYr+2yt5X7PdemOLP9BXgD8Az9vPHgKvsx78BbrEf3wr8xn58FfCo/XiefY2ygen2tfMOxXUElgM32Y+zgOKReG2ASuADIDfsmlw3kq4N8GFgEbApbFvKr0W0c6Tgs5wHZNiPfxj2WeL+m8d7XZN2jVL5ZUzVD3Aq8ELY868DXx/qcUUZ65PAucB2oMLeVgFstx/fC3wqbP/t9uufAu4N236vva0C2Ba2vdd+KRj/JOBl4CzgGfvLVh/2jx+6FlgZaKfajzPs/STy+jj7DfZ1BIqwJlWJ2D7irg2WQOzHmhgz7Gtz/ki7NsA0ek+qKb8W0c6R7M8S8doy4GG3v2V/f/NEvnPJuj4j1cXkfDkcDtjbhhW2ubcQeAeYYIyptl+qASbYj6N9lqNtP+CyPVX8DLgDcBazGAs0GmOcBTDCzx8as/16k71/vJ8xVUwH6oAHxXKZ3S8i+YzAa2OMqQJ+DOwDqrH+1u8ycq+Nw2Bci2jnSCU3YFkxEP9nSeQ7lxRGqkAMe0RkDPAE8CVjTHP4a8aS+2GfXywiFwO1xph3h3osSSIDyw1wjzFmIdCG5WIIMYKuTQlWM8vpwEQgH7hgSAeVZAbjWgzGOUTkm4AfeDiV50kFI1UghnVbDhHJxBKHh40xK+zNh0Skwn69Aqi1t0f7LEfbPslleyo4HbhERPYAj2C5mX4OFIuIU2QZfv7QmO3Xi4DDxP8ZU8UB4IAx5h37+eNYgjESr805wAfGmDpjjA9YgXW9Ruq1cRiMaxHtHElHRK4DLgauscWIfsbstv0w8V/X5JBsn+Jg/GDdCe7Guntygjnzh3pc9tgE+B3ws4jt/03vwNiP7McX0Tv4tsreXorlLy+xfz4ASu3XIoNvHxuEz/VReoLUf6J3wOxW+/Hn6R0we8x+PJ/eQbndWAG5Qb+OwN+BOfbj79jXZcRdG+BkYDOQZ59rOfDFkXZt6BuDSPm1iHaOFHyWC7CWKyiL2C/uv3m81zVp1yeVX8ZU/mBlNbyPFfX/5lCPJ2xcZ2CZrBuA9fbPx7D8gi8DO4CXwv6JBWuhpF3ARmBx2LFuAHbaP9eHbV+M1eZ8F/BLkhiUOsrn+ig9AjHD/vLttP9xs+3tOfbznfbrM8Le/017vNsJy+wZ7OsInAissa/Pn+1JZUReG+C7wDb7fL+3J5wRc22AP2LFT3xY1t2Ng3Etop0jBZ9lJ1Z8wJkHfpPo3zyR65qMH221oSiKorgyUmMQiqIoSopRgVAURVFcUYFQFEVRXFGBUBRFUVxRgVBGDWI16rt1qMehKCMFFQhlNFGM1dwsJuwOosPmOxJWKKUog8Kw+edXlEHgLmCmiKy3WzHfLiKr7XbM3wWrf5bdbvl3WDn0k0Wk1d5/s4i8JCJLReQ1EdktIpdEO5mIzBeRVfb5NojIbHv7Z+zn74nI78PO+4q9/WURmWJvf0hEfiMi7wA/EpF8u7X0Kruf1KWp/qMpoxetg1BGDXbzxGeMMQtE5Dzg48BnsYqwnsJqA70Pq5r1NGPM2/b7DFYV7nMishKr79FFWG2blxtjToxyvruBt40xD9v9+71YBU8r7ePXi0ipMeaIiDwNPG6MWS4iNwCXGGMuE5GHgHHApcaYgIj8ANhijPk/e32BVcBCY0xb0v9gyqhHTVZltHKe/bPOfj4GmI0lEHsdcbDpBp63H28EuowxPhHZiNVeIRpvAd8UkUnACmPMDhE5C/iTMaYewBhzxN73VOBy+/HvscTK4U/GmEDYuC8Rkf9nP88BpgBbY/vYihI7KhDKaEWA/zLG3Ntro2VlRN6N+0yPqR0EugCMMcGjxQWMMX+wXUMXAc+KyGcTHGv4eAS4whizPcFjKUrMaAxCGU20YK3yB9ZCKzfYbdkRkUoRGZ/Mk4nIDGC3MeYXWAtHHQ+8AnxCRMba+5Tau/8Dq9kawDVYTQXdeAH4omBsDuAAAAC8SURBVEho+cyFyRyzooSjFoQyajDGHBaRN+11g5/DWkb1LXuubQU+DQSOcoh4uRL4ZxHxYS1M8wM73vCfwOsiEsBycV2H1Yn1QRG5HWtRo+ujHPN7WIs4bbAzrD7AaietKElHg9SKoiiKK+piUhRFUVxRF5OiDBAROR/4YcTmD4wxy4ZiPIqSLNTFpCiKoriiLiZFURTFFRUIRVEUxRUVCEVRFMUVFQhFURTFFRUIRVEUxRUVCEVRFMWV/w9KId/t66cYKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.sort_values(by='term_score', ascending = False)\n",
    "\n",
    "plt.plot(data['term_score'].values, data['%coverage'].values)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('term_score')\n",
    "plt.ylabel('%coverage')\n",
    "\n",
    "plt.axis([0,130000,0,100])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.37280841],\n",
       "       [0.37280841, 1.        ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(data['term_score'],data['EPMC%coverage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = data.sort_values(by='idf_score', ascending = False)[11:]\n",
    "plt.plot(data['idf_score'].values, data['EPMC%coverage'].values)\n",
    "\n",
    "# Label the axes\n",
    "plt.xlabel('idf_score')\n",
    "plt.ylabel('%coverage')\n",
    "\n",
    "plt.axis([1,11.1,0,100])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.39354591],\n",
       "       [-0.39354591,  1.        ]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(data['idf_score'],data['EPMC%coverage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}